Lesbian Motherhood and Artificial Reproductive Technologies in North America: Race, Gender, Kinship, and the Reproduction of Dominant Narratives
Bibliographic record
Abstract
This paper reviews current ethnographic literature on lesbian motherhood as it relates to artificial reproductive technologies (ART) through intersectional, biopolitical and critical-race frameworks. I argue that white, lesbian intending mothers intersecting identity markers of whiteness and queerness place them in a unique position within ART discourses. ART functions as a biopolitical mechanism which aims to normalize and naturalize privilege in hierarchized power structures, while suggesting that the meanings that it produces are objectively scientific rather than socially constructed. I suggest that ART mechanizes white lesbian women’s insecurities as queer women, nearing the falsified construction of ideal motherhood, by exerting pressure on them to conform and therefore, reproduce dominant reproduction narratives. Simultaneously, I assert that white, lesbian, intending mothers’ positionality could enable critical interrogation into the harmful social stratifications that ART perpetuates based on race, class, ability, and sexuality. In sum, a review of relevant literature is used to posit that women privileged within dominant ART discourses must utilize that privilege to create meaningful change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".